AI-Powered Crime Analysis: Predicting Trends and Visualizing Insights

Crime Analyst AI

In an era where data drives decision making, the ability to predict and analyze crime trends has become a critical tool for law enforcement, urban planners, and policymakers. Traditional methods of crime analysis often rely on historical data and manual interpretation, which can be time consuming and prone to human error. Enter AI-powered crime analysis—a cutting edge approach that leverages machine learning and advanced data visualization to transform raw crime data into actionable insights.

In this article, we’ll explore a Python based script that uses AI to predict crime trends, validate predictions against historical data, and visualize insights on interactive maps. This tool is not just a technical achievement but a practical solution for addressing real world challenges in public safety.

The Problem: Crime Analysis in the Modern World

Crime is a complex phenomenon influenced by a multitude of factors, including socioeconomic conditions, geographic location, and historical patterns. Analyzing crime data manually is a daunting task, especially when dealing with large datasets spanning multiple years and regions. Traditional methods often fail to capture the intricate relationships between these factors, leading to incomplete or inaccurate predictions.

To address these challenges, we need a system that can:

• Process large datasets efficiently.
• Identify patterns and trends that are not immediately obvious.
• Generate actionable insights in a format that is easy to understand and act upon.

The Solution: AI-Powered Crime Analysis

The script we’ll discuss today is a Python-based tool that combines the power of AI models (specifically, the Llama 3.2 model) with data visualization libraries like folium and pandas to create a comprehensive crime analysis system. Here’s how it works:

1. Data Ingestion

The script begins by reading crime data from a file (either .csv or .xlsx). This data typically includes fields like latitude, longitude, crime type, and timestamps. The pandas library is used to handle the data efficiently, even for large datasets.

def read_crime_data(file_path):
    file_extension = os.path.splitext(file_path)[1].lower()
    if file_extension == '.csv':
        chunks = pd.read_csv(file_path, encoding='ISO-8859-1', chunksize=10000)
        return pd.concat(chunk for chunk in chunks)
    elif file_extension == '.xlsx':
        return pd.read_excel(file_path)
    else:
        raise ValueError("Unsupported file format. Please use a .csv or .xlsx file.")

2. Predictive Analysis with AI

The core of the script is the Ollama AI model, which is used to predict future crime hotspots based on historical data. The model takes a prompt that describes the task and the data, and it outputs predictions in a structured format.

def run_ollama_predictive_model(prompt, data_for_model):
    try:
        process = subprocess.run(
            ['ollama', 'run', 'llama3.2', prompt],
            capture_output=True,
            text=True,
            check=True
        )
        logging.info("Ollama model ran successfully")
    except subprocess.CalledProcessError as e:
        logging.error(f"Error running Ollama model: {e}")
        logging.error("Ollama stderr:")
        logging.error(e.stderr)
        raise
    output = process.stdout
    logging.debug("Ollama stdout:")
    logging.debug(output)
    if not output.strip():
        raise ValueError("The Ollama model output is empty. Please check the model and try again.")
    return output

3. Insight Extraction

The script then extracts insights from the AI model’s output. These insights include predicted crime locations, types, and likelihoods. The extracted data is structured into a format that can be easily visualized and analyzed.

def extract_insights_from_output(output):
    insights = []
    lines = output.split('\n')
    for line in lines:
        if "latitude:" in line.lower() and "longitude:" in line.lower():
            try:
                parts = line.split(',')
                latitude = longitude = None
                crime_type = "Unknown"
                prediction = "No prediction"
                likelihood = "Unknown"
                for part in parts:
                    if "latitude:" in part.lower():
                        latitude = float(part.split(':')[-1].strip())
                    if "longitude:" in part.lower():
                        longitude = float(part.split(':')[-1].strip())
                    if "crime type:" in part.lower():
                        crime_type = part.split(':')[-1].strip()
                    if "prediction:" in part.lower():
                        prediction = part.split(':')[-1].strip()
                    if "likelihood:" in part.lower():
                        likelihood = part.split(':')[-1].strip()
                if latitude is not None and longitude is not None:
                    insights.append({
                        "Latitude": latitude,
                        "Longitude": longitude,
                        "CrimeType": crime_type,
                        "Prediction": prediction,
                        "Likelihood": likelihood
                    })
            except ValueError:
                continue
    return insights

4. Validation and Visualization

The script validates the predictions against historical data to ensure accuracy. It also generates an interactive map using the folium library, which visualizes both actual and predicted crime hotspots.

def create_crime_map(actual_data, insights, output_file='crime_analyst_ai_map.html'):
    map_center = [33.75, -84.5]
    crime_map = folium.Map(location=map_center, zoom_start=10)

    from folium.plugins import HeatMap
    heat_data_actual = [[row['Latitude'], row['Longitude']] for _, row in actual_data.iterrows()]
    HeatMap(heat_data_actual).add_to(crime_map)

    for _, row in actual_data.iterrows():
        folium.Marker(
            location=[row['Latitude'], row['Longitude']],
            popup=f"{row['CrimeType']} - Actual Data",
            icon=folium.Icon(color='green')
        ).add_to(crime_map)

    for insight in insights:
        folium.Marker(
            location=[insight['Latitude'], insight['Longitude']],
            popup=folium.Popup(f"<b>{insight['CrimeType']}</b><br>Prediction: {insight['Prediction']}<br>Likelihood: {insight['Likelihood']}", max_width=250),
            icon=folium.Icon(color='purple', icon='info-sign')
        ).add_to(crime_map)

    crime_map.save(output_file)
    logging.info(f"Map has been created and saved as '{output_file}'.")

Why This Matters

This script is more than just a technical tool, it’s a step toward smarter, data driven decision making in public safety. By combining AI with intuitive visualizations, it empowers users to:

• Identify high risk areas and allocate resources more effectively.
• Validate predictions against historical data to ensure accuracy.
• Communicate insights clearly through interactive maps and reports.

Future Enhancements

While the script is already powerful, there are several ways to enhance its capabilities:

• Advanced Machine Learning Models: Experiment with other AI models to improve prediction accuracy.
• User Friendly Interface: Develop a web based interface to make the tool accessible to non-technical users.

Conclusion

The AI-powered crime analysis script is a testament to the transformative potential of technology in addressing real-world challenges. By leveraging AI, data visualization, and predictive analytics, it provides a robust framework for understanding and combating crime. Whether you’re a data scientist, law enforcement professional, or urban planner, this tool offers a glimpse into the future of public safety—one where data driven insights lead to safer, more resilient communities.

Try It Yourself

If you’re interested in exploring the script, you can find the complete code on GitHub. Feel free to fork the repository, experiment with the code, and contribute to its development. Together, we can harness the power of AI to create a safer world.

Application developed by Eric Maddox. 2024